
'''simple docstring'''

import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase :   Union[str, Any]			    =	logging.get_logger(__name__)


_UpperCAmelCase :   Dict			    =	{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

_UpperCAmelCase :   Any			    =	{
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

_UpperCAmelCase :   Optional[int]			    =	{'facebook/blenderbot_small-90M': 512}







def     snake_case__						(   UpperCamelCase						)			->   Tuple:
		_UpperCamelCase							:			Dict     =   set()
		_UpperCamelCase							:			Any     =   word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)						)
				_UpperCamelCase							:			Dict     =   char

		_UpperCamelCase							:			int     =   set(_A						)
		return pairs


class       UpperCAmelCase					(       lowerCamelCase__			):





		"""simple docstring"""





		A__	: int    =     VOCAB_FILES_NAMES
		A__	: List[Any]    =     PRETRAINED_VOCAB_FILES_MAP
		A__	: Optional[Any]    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A__	: int    =     ['input_ids', 'attention_mask']
		def __init__(     self	,       _snake_case	,       _snake_case	,       _snake_case="__start__"	,       _snake_case="__end__"	,       _snake_case="__unk__"	,       _snake_case="__null__"	,       **_snake_case	,       )					->     Tuple:
				super().__init__(unk_token=__snake_case	,       bos_token=__snake_case	,       eos_token=__snake_case	,       pad_token=__snake_case	,       **__snake_case  )

				with open(__snake_case	,       encoding='''utf-8'''  ) as vocab_handle:
						_UpperCamelCase							:			Any     =   json.load(__snake_case  )
				_UpperCamelCase							:			Any     =   {v: k for k, v in self.encoder.items()}
				with open(__snake_case	,       encoding='''utf-8'''  ) as merges_handle:
						_UpperCamelCase							:			Optional[Any]     =   merges_handle.read().split('''\n'''  )[1:-1]
				_UpperCamelCase							:			int     =   [tuple(merge.split()  ) for merge in merges]
				_UpperCamelCase							:			Any     =   dict(zip(__snake_case	,       range(len(__snake_case  )  )  )  )
				_UpperCamelCase							:			str     =   {}
		@property
		def _lowercase	(     self  )					->     Optional[Any]:
				return len(self.encoder  )
		def _lowercase	(     self  )					->     List[Any]:
				return dict(self.encoder	,       **self.added_tokens_encoder  )
		def _lowercase	(     self	,       _snake_case  )					->     Any:
				if token in self.cache:
						return self.cache[token]
				_UpperCamelCase							:			List[str]     =   re.sub('''([.,!?()])'''	,       r''' \1'''	,       __snake_case  )
				_UpperCamelCase							:			Tuple     =   re.sub('''(\')'''	,       r''' \1 '''	,       __snake_case  )
				_UpperCamelCase							:			Optional[int]     =   re.sub(r'''\s{2,}'''	,       ''' '''	,       __snake_case  )
				if "\n" in token:
						_UpperCamelCase							:			Optional[int]     =   token.replace('''\n'''	,       ''' __newln__'''  )

				_UpperCamelCase							:			int     =   token.split(''' '''  )
				_UpperCamelCase							:			Dict     =   []
				for token in tokens:
						if not len(__snake_case  ):
								continue

						_UpperCamelCase							:			str     =   token.lower()
						_UpperCamelCase							:			Dict     =   tuple(__snake_case  )
						_UpperCamelCase							:			int     =   tuple(list(word[:-1]  ) + [word[-1] + '''</w>''']  )
						_UpperCamelCase							:			List[Any]     =   get_pairs(__snake_case  )

						if not pairs:
								words.append(__snake_case  )
								continue

						while True:
								_UpperCamelCase							:			str     =   min(__snake_case	,       key=lambda _snake_case  : self.bpe_ranks.get(__snake_case	,       float('''inf'''  )  )  )
								if bigram not in self.bpe_ranks:
										break
								_UpperCamelCase							:			Dict     =   bigram
								_UpperCamelCase							:			int     =   []
								_UpperCamelCase							:			Dict     =   0

								while i < len(__snake_case  ):
										try:
												_UpperCamelCase							:			List[Any]     =   word.index(__snake_case	,       __snake_case  )
												new_word.extend(word[i:j]  )
												_UpperCamelCase							:			Any     =   j
										except ValueError:
												new_word.extend(word[i:]  )
												break

										if word[i] == first and i < len(__snake_case  ) - 1 and word[i + 1] == second:
												new_word.append(first + second  )
												i += 2
										else:
												new_word.append(word[i]  )
												i += 1
								_UpperCamelCase							:			Tuple     =   tuple(__snake_case  )
								_UpperCamelCase							:			List[Any]     =   new_word
								if len(__snake_case  ) == 1:
										break
								else:
										_UpperCamelCase							:			Tuple     =   get_pairs(__snake_case  )
						_UpperCamelCase							:			int     =   '@@ '.join(__snake_case  )
						_UpperCamelCase							:			Union[str, Any]     =   word[:-4]

						_UpperCamelCase							:			Any     =   word
						words.append(__snake_case  )
				return " ".join(__snake_case  )
		def _lowercase	(     self	,       _snake_case  )					->     List[Any]:
				_UpperCamelCase							:			Optional[Any]     =   []

				_UpperCamelCase							:			str     =   re.findall(r'''\S+\n?'''	,       __snake_case  )

				for token in words:
						split_tokens.extend(list(self.bpe(__snake_case  ).split(''' '''  )  )  )
				return split_tokens
		def _lowercase	(     self	,       _snake_case  )					->     str:
				_UpperCamelCase							:			List[Any]     =   token.lower()
				return self.encoder.get(__snake_case	,       self.encoder.get(self.unk_token  )  )
		def _lowercase	(     self	,       _snake_case  )					->     Union[str, Any]:
				return self.decoder.get(__snake_case	,       self.unk_token  )
		def _lowercase	(     self	,       _snake_case  )					->     List[Any]:
				_UpperCamelCase							:			Optional[int]     =   ' '.join(__snake_case  ).replace('''@@ '''	,       ''''''  ).strip()
				return out_string




		def _lowercase	(     self	,       _snake_case	,       _snake_case = None  )					->     Optional[int]:
				if not os.path.isdir(__snake_case  ):
						logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
						return
				_UpperCamelCase							:			List[str]     =   os.path.join(
				    __snake_case	,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )
				_UpperCamelCase							:			Union[str, Any]     =   os.path.join(
				    __snake_case	,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']  )

				with open(__snake_case	,       '''w'''	,       encoding='''utf-8'''  ) as f:
						f.write(json.dumps(self.encoder	,       indent=2	,       sort_keys=__snake_case	,       ensure_ascii=__snake_case  ) + '''\n'''  )

				_UpperCamelCase							:			Any     =   0
				with open(__snake_case	,       '''w'''	,       encoding='''utf-8'''  ) as writer:
						writer.write('''#version: 0.2\n'''  )
						for bpe_tokens, token_index in sorted(self.bpe_ranks.items()	,       key=lambda _snake_case  : kv[1]  ):
								if index != token_index:
										logger.warning(
										    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
										    ''' Please check that the tokenizer is not corrupted!'''  )
										_UpperCamelCase							:			Union[str, Any]     =   token_index
								writer.write(''' '''.join(__snake_case  ) + '''\n'''  )
								index += 1

				return vocab_file, merge_file










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase:   List[Any]					=		{'configuration_reformer': ['REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ReformerConfig']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   Dict					=		['ReformerTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   Tuple					=		['ReformerTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   List[Any]					=		[
         'REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'ReformerAttention',
         'ReformerForMaskedLM',
         'ReformerForQuestionAnswering',
         'ReformerForSequenceClassification',
         'ReformerLayer',
         'ReformerModel',
         'ReformerModelWithLMHead',
         'ReformerPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_reformer import ReformerTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_reformer_fast import ReformerTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_reformer import (
              REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              ReformerAttention,
              ReformerForMaskedLM,
              ReformerForQuestionAnswering,
              ReformerForSequenceClassification,
              ReformerLayer,
              ReformerModel,
              ReformerModelWithLMHead,
              ReformerPreTrainedModel,
          )

else:
     import sys

     lowerCAmelCase:   str					=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)